摘要：
When dealing with a dynamic causal system people may employ a variety
of different strategies. One of these strategies is causal learning,
that is, learning about the causal structure and parameters of the
system acted upon. In two experiments we examined whether people
spontaneously induce a causal model when learning to control the
state of an outcome value in a dynamic causal system. After the
control task, we modified the causal structure of the environment
and assessed decision makers’ sensitivity to this manipulation.
While purely instrumental knowledge does not support inferences
given the new modified structure, causal knowledge does. The results
showed that most participants learned the structure of the underlying
causal system. However, participants acquired surprisingly little
knowledge of the system’s parameters when the causal processes
that governed the system were not perceptually separated (Experiment
1). Knowledge improved considerably once processes were separated
and feedback was made more transparent (Experiment 2). These findings
indicate that even without instruction, causal learning is a favored
strategy for interacting with and controlling
a dynamic causal system.
